An optimal preventive maintenance strategy for the hydraulic system of platform firefighting vehicle based on the improved NSGA-II algorithm. (December 2019)

• Record Type: Journal Article
• Authors: Teng, Rumin; Liang, Jifei; Wu, Guanglei; Wang, Dianlong; Wang, Xin
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 233:Number 6(2019:Dec.)
• Page Start: 978
• Page End: 989
• Publication Date: 2019-12
• Subjects: Platform firefighting vehicle -- hydraulic system -- preventive maintenance -- NSGA-II algorithm -- reliability analysis; Reliability (Engineering) -- Mathematical models -- Periodiclals; Risk assessment -- Mathematical models -- Periodicals; Engineering design -- Mathematical models -- Periodicals; 620.00452
• DOI: 10.1177/1748006X19849752
• Languages: English
• ISSNs: 1748-006X

Abstract

Based on the reliability analysis, this work presents a model of the preventive maintenance for the hydraulic system of platform firefighting vehicle, considering the working age regression factor and the equivalent service life. In this model, both the equipment availability and average maintenance cost are treated as the objective functions and constrained by the equipment reliability as well as the total working time. The non-dominated sorting genetic algorithm II (NSGA-II) algorithm is adopted to solve the previous optimization problem, wherein the constrained inequality functions in the stages of initialization, mutation, and crossover are introduced to assess the solution to meet the constraints or not, through the improvements in three aspects, namely, the improvement of the algorithm's initialization to ensure the initial variables meet the equality constraint, the utilization of the mean division of the difference values to adjust the variables after the selection, and crossover and mutation of the gene. Numerical analysis is carried out to verify the feasibility of the improved algorithm, wherefrom the Pareto-optimal solutions for the preventive maintenance of the platform firefighting vehicle are obtained to provide the optimum strategy for the service engineers with different weighted factors.